Retrieval-augmented generation for educational application: A systematic survey
Zongxi Li, Zijian Wang, Weiming Wang et al.
2025 · Computers and Education Artificial Intelligence · 87 citations
Advancements in large language models (LLMs) have transformed AI-driven education, enabling innovative applications across various learning and teaching domains. However, LLMs still face several challenges, including hallucination and static internal knowledge, which hinder their reliability in educational settings. Retrieval-Augmented Generation (RAG) enhances LLMs by retrieving relevant information from an external knowledge base and incorporating it into the LLM's generation process. This approach improves factual accuracy and enables dynamic knowledge updates, making LLMs particularly sui…
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